In addition this work proposed an influential framework for designing style transfer objective functions. In this framework (described in detail in Section ) objective functions consist of two terms, a `style loss' \(\mathcal {L}_s(O,S)\)  which measures the stylistic similarity between the output image \(O\)  and the style image \(S\) , and a `content loss' \(\mathcal {L}_c(O,C)\)  which measures the similarity in semantic content and layout between \(O\)  and the content image \(C\) . The original objective function proposed by  [1]} defined style loss based on the Gram matrices of features extracted from early layers (i.e. the local correlation or co-occurrence of simple visual features with small spatial extent). The content loss was based on the difference between the output image and the style image in the feature space of a deeper layer of the same network. The intuition being that, to facilitate generalization, features deep in an image classification network should be stable under changes to local texture or color. Proposing alternate definitions for one or both of these terms, in other words refining the quantitative definitions of content and style, has been one of the main forms of innovation in subsequent work.
